Univariate GARCH Models Research Articles

Volatility of Fish Prices in Manado City Using Multivariate GARCH Model

Time series data modeling is generally performed using the assumption of homoscedasticity or residual variance that is constant over time. However, the homoscedasticity assumption cannot answer the problem of volatility in economic and business time series data, because generally data on economics and business have residual variances that always change over time. Therefore, a model developed with the assumption of not constant variance is known as heteroskedasticity model. Multivariate GARCH model is a development of the univariate GARCH model. The multivariate GARCH model can be viewed as a conditional heteroskedasticity model in a multivariate time series. This paper discusses the parameterization of covariance matrices such as Vech model representation, BEEK model and Constant Correlation model. For parameter estimation the maximum likelihood method is used. Furthermore, multivariate GARCH model application is applied for multivariate model. In this research, the data used are data on the prices of fresh fish in Manado. The fish price data is the price of Tuna, Skipjack (Cakalang), Cobe (Tongkol), Kite (Malalugis) and Selar fish (Tude). The ARCH effect test results show that the data meet the assumption of non-constant variance (heteroscedasticity). GARCH mutivariate modeling in the form of a VEC diagonal model gives the results of the price volatility of fresh fish in Manado

Oil and Stock Markets in Ongoing Flux: Impact of Current Events on Oil Price and Stock Market Performance

Abstract The price of oil and, indirectly, the performance of stock indices have been significantly affected by the events of recent years, with a negative impact on the global economy, including the evolution of the energy market in the context of the transition to renewable energy sources and changes in global energy policy. This paper proposes a comprehensive analysis of the evolution of stock indices, oil market volatility and investors' response to the "black swan" events that have recently caused changes in the global economy. Using advanced econometric and statistical models, the paper presents the complex relationship between the analyzed stock indices, the trends and changes that occurred within them and the relevant influencing factors, such as the price of Brent Oil and the Baltic Dry Index. The results obtained, based on univariate GARCH models (GJR-GARCH) and multivariate DCC-GARCH models are in accordance with specialized studies in the field and show that oil price fluctuations were higher at the beginning of the Covid-19 pandemic compared to the period after the military conflicts (stage in which no significant influence of the oil price on the stock market is observed). Also, using the Chow test, in the analyzed period there are 3 important breaking points that coincide with the dates when the COVID-19 pandemic began, the military conflict between Russia and Ukraine as well as the military conflict between Israel and Gaza, events with a strong impact on the economy. The obtained results also show another important aspect, namely that, during the selected period January 2019-November 2023, the price of oil and the stock market were much more affected by the COVID-19 pandemic than by military conflicts, which cannot be also stated in the case of the price of the Baltic-Dry Index.

Market Shocks and Stock Volatility: Evidence from Emerging and Developed Markets

Market turbulences and their impact on the financial market, particularly on the stock market, is a financial topic that has received significant research attention recently. This study compared the characteristics of stock return and volatility in selected developed and emerging markets between the 2008 financial crisis and the 2019 worldwide pandemic. In this sense, we seek to answer two concerns. First, do the developed and emerging markets behave similarly during crisis periods? Second, does economic strength always shield markets from poor economic circumstances? For this purpose, the daily return data of E7 (Emerging 7) and G7 (Developed 7) countries for two sample periods—namely, the financial crisis period of 2007–2009 and the global pandemic period of 2019–2021—were chosen. By using univariate GARCH models, namely GARCH, EGARCH, and TGARCH, the study discovered that developing and developed markets reacted differently to these two financial crises. While emerging markets responded similarly to these two crises, developed economies acted differently, being more volatile and sensitive to the worldwide pandemic of 2019 than the financial crisis of 2008. Moreover, a country’s economic prowess does not always shield it from economic turmoil. This study will help investors identify diversification opportunities among the developed and emerging markets during a crisis period. Additionally, this will help portfolio and fund managers understand the behaviour of stock markets during times of market crisis and thus give advice to investors.

Sự kiện chính trị và biến động thị trường chứng khoán: Nghiên cứu thực nghiệm từ các nước Đông Nam Á

This study investigates the impact of political events on the stock market volatility in South-East Asia countries from 2010 to 2019. Using univariate and multivariate GARCH models with dummy variables to study the behaviour of the market before and after the event, we find that the volatility of stock market decreases significantly after the political reform. This finding is important in understanding the behaviour of the stock market and may benefit to market participants and regulators

Comparing the Performance of GARCH Family Models in Capturing Stock Market Volatility in India

In recent times, the prediction of stock market volatility has emerged as a central focus in the domain of financial econometrics. This paper presents an empirical analysis aimed at modelling the volatility of the Indian stock market, particularly focusing on the NSE NIFTY 50, by utilizing various GARCH models. The investigation explores the volatility of stock returns, considering the daily closing prices, and examines the influence of two external factors: Crude oil prices and the INR/USD exchange rate. The inquiry employs data encompassing the period from January 1, 2012, to December 31, 2022, for all three variables. The manuscript delves into an array of univariate GARCH models, encompassing both symmetric and asymmetric models, and assesses their performance by utilizing metrics such as the Akaike Information Criterion, Schwartz Bayesian Information Criterion, and Log Likelihood. To assess the predictive accuracy of these models, statistical error measures such as Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are employed. The findings strongly suggest that the EGARCH model is the most effective in predicting the variations of the NIFTY index. Furthermore, the research highlights the significant impact of exchange rates and crude oil prices in relation to the volatility of the stock market in India.

Shifting the paradigm for securing efficient management of exchange rate in Nigeria: A GARCH and BEKK-MGARCH analysis

The influence of foreign exchange on all spheres of any economy is germane to determining its extent of development. Most developing countries have been subjected to managing the integrity of the exchange rate arising from their disadvantageous position in world trade. Nigeria is not an exemption either; this is due to its constant interventions in the foreign exchange market from time to time and by the policy of the existing government in power. However, the interventionist policy framework has been more harmful to Naira as the currency depreciates unabatedly. The pertinent question is whether monetary policy should be the only approach to effective exchange rate management or be combined with fiscal and income policies. Irrespective of the policy choice, the aim, target, and instrument must be complementary without unnecessary disruptions midway, as usually experienced in Nigeria. These inconsistencies have come to bear enormously on the country's exchange rate management. Rather than focusing on the impact of exchange rate volatility on industrial development that could warrant foreign exchange inflows, this paper considers industrialization as an approach to effectively managing exchange rates in Nigeria. This analysis employs a univariate GARCH and BEKK-MGARCH model, using high-frequency monthly time series data from 2000 to 2019, to examine the volatility transmission between the foreign exchange market and the industrial sector. The model estimation uses the Conditional Maximum Likelihood Technique (CMLT). It was discovered that industrial development positively influenced the official market exchange rate compared to the parallel market exchange rate, reducing its volatility. The study thus suggests the vigorous pursuit of foreign exchange earnings and usage policy by earning entities, floating Diaspora bonds, enhancing and encouraging remittances and repatriation of illicit financial outflows to enhance industrialization. This will discourage arbitraging, increasing foreign exchange inflows and stabilizing the economy.

Pattern, Source, Destination of Volatilities in Financial Market and Policy Lessons

This paper studies patterns of volatilities and their spillovers across six major segments of Indian financial market applying univariate GARCH model and estimating the Diebold and Yilmaz (DY) volatility spillover index. The study found increasing integration of financial market segments over time and that equity, bank index and corporate bond segments are net contributors while money, gsec and forex segments are net receivers of volatility. The study highlights the need for a healthy banking sector and the increasing significance of corporate bond market volatility and suggests segment specific policy responses to tackle financial market volatilities.

How important is green awareness in energy investment decisions? An environmentally-based rebalancing portfolio study

This paper empirically studies the financial performance of energy asset allocation strategies under green and brown preferences. We assume that green investors and traditional or brown energy investors behave differently in their investment decisions. Thus, our optimal portfolio choice is developed in several steps for each energy investor profile and for each monthly rebalancing period. First, energy companies are screened based on Environmental (E) rating quartiles. We construct two categories formed via those companies rated above the first quartile, Q1, and below the third quartile, Q4. Second, for each category, a dynamic minimum variance (MV) problem is optimized based on univariate and multivariate GARCH models. Then, we assess the performance of the whole investment strategies posed. This paper contributes to the financial literature by providing evidence that not only does green investment help reduce the carbon footprint, but also that there is a risk-return spread between green and brown energy investment, adding value for the environmentally concerned investor.

Bitcoin, Gold and Crude Oil versus the US Dollar – A GARCH Volatility Analysis

Abstract The aim of this study is to compare and contrast the volatility of different asset classes namely Bitcoin, gold and crude oil against the US dollar, using the symmetric GARCH (1,1) model. Furthermore, this study examines which of the three assets provide the lowest volatility and identifies if the univariate GARCH (1,1) model can suitably forecast the volatility of the foreign exchange market, commodity market and cryptocurrency market. More specifically, this study uses only estimates from a symmetric GARCH model for the XAU/USD, WTI/USD and BTC/USD financial assets. Although the literature on the volatility of different assets is extensive, it neglects to detect the severe economic recession that is approaching. Considering that powerful nations purchased substantial quantities of gold in order to back their national currency, supremacy of the US dollar is under significant attack. Experts in international relations have claimed that it is essential to have a single, extremely influential national economy to exhibit stability and operate smoothly. Specifically, the international monetary system functioned under the theory of hegemonic stability. Given the fact that gold still remains the safe haven asset during periods of financial and economic distress, central banks are purchasing gold at a rapid pace with Russia and China leading the way. Furthermore, the demand for precious metals has also increased while the US dollar is struggling to keep its supremacy as BRICS reserve currency is seeking to replace it in the future. The daily data encompassing the necessary information for February 2012 - February 2020 is acquired from “Investing.com”, reaching 7193 observations. This study will enrich the literature associated with volatility forecasting of different asset classes during financial and economic turmoil while also raising awareness of the economic threats that could follow.

Modeling Indian Bank Nifty volatility using univariate GARCH models

The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.

Modelling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India

Does Indian sovereign yield volatility reflect economic fundamentals, or whether it is a self-generated force flowing through markets with little connection to such fundamentals? To answer the question, this research explores the volatility dynamics and measures the persistence of shocks to the sovereign bond yield volatility in India from 1 January 2016, to 18 May 2022, using a family of GARCH models. The empirical results indicate the high volatility persistence across the maturity spectrum in the sample period. However, upon decomposing the markets into bull and bear phases, our results support the existence of weak volatility persistence and rapid mean reversion in the bear market. This shows that the economic response policies implemented by the government during the pandemic, including fiscal measures, have a restraining effect on sovereign yield volatility. For a positive γ, the results suggest the possibility of a “leverage effect” that is markedly different from that frequently seen in stock markets. Results further indicate that the fluctuations in Indian sovereign yields cannot be dissociated from inflation and money market volatility. Our findings herein provide valuable information and implications for policymakers and financial investors worldwide.

Estimation of stock market index volatility using the GARCH model: Causality between stock indices

This paper aims to model the volatility of returns for selected stock indices and examine the causal relationships between the markets using the historical daily prices of the Financial Times Stock Exchange (FTSE), Bursa Malaysia Kuala Lumpur Composite Index (KLCI), the Indonesia Stock Exchange Index (LQ45), and the Stock Exchange of Thailand (SET) from January 2008 to November 2019. The study employs univariate GARCH models that are prominent in capturing the volatility clustering of financial instruments in association with the Box–Jenkins methodology for better estimation. Generally, the ARMA-GARCH model is used to capture the volatility series, while the Granger causality test examines the causal directions between the markets. The findings revealed leverage effects on the markets, with the outperformance of the EGARCH in analyzing the empirical properties of stock returns. An initial test that yielded positive correlations suggests the existence of co-movement between the derived volatility series. The study concluded bidirectional causal relationships between the selected markets, and based on the resulting relationships, it is proposed that supervision of markets among the ASEAN members could be advantageous in predicting the corresponding market performance.

Modelling Asymmetric Sovereign Bond Yield Volatility with Univariate GARCH Models: Evidence from India

Modelling Asymmetric Sovereign Bond Yield Volatility with Univariate GARCH Models: Evidence from India

Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective

In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market.

ESG Green Equity Finance Risk and Links in Mexico: Conditional Volatility and Markov Switching Vector Analyses

We analyze the differential influence of Mexican oil price, exchange rate and S&P 500 Index on the Mexican Stock Exchange: S&P/BMV IPC ESG Tilted Index (sustainable stock market index), and on the S&P/BMV IPC (General stock market index) in two different regimes. First, we estimate the conditional volatility of the series using a univariate GARCH model under the t-Student distribution. Second, a Markov Switching Vector Autoregressive model is developed. The evidence identifies sustainable asset performance, risk, and interaction with other regular assets. The sustainable index is more vulnerable to the foreign exchange market and to the U.S. stock market, especially in periods of turbulence. Only the S&P 500 shows a statistically significant impact on the overall Mexican index, for both states high and low volatility. Oil prices do not have a significant impact on the Mexican indices analyzed. Evidence allows us to recommend a currency hedging in ESG investments. Originality relies on empirical approaches and the study of ESG index in an emerging market. Limitations are related with the scarce information and limited access to ESG factors data. We conclude that investment strategies should be different during calm and turmoil periods.

Estimating the Effect of the US-China Trade War on the Relationship between the Prices in the International Feed Grain Futures Markets and China’s Pork Market

We examine the effect of the US-China trade war on the international feed grain futures markets and China’s pork market by using the univariate GARCH model with considering the structural breaks in each market. Granger causality test and the DCC-GARCH model are applied to examine the changes in the relationship between the international feed grain futures market and China’s pork market in the presence of the trade war. The analytical results suggest that the trade war has a significant negative effect on the volatility of international soybean futures price, but it exacerbated volatility in Chinese pork prices. Our empirical results also confirm that a stable causality existed between international soybean futures market and China’s pork market in the pre-trade war period, but it was disrupted after the trade war broke out.

Managing Exchange Rate Volatility during Political Crises

The goal of this paper is to show how the exchange rate volatility could be managed during political crises, using as a case study the fragile
 the political relationship between USA and Turkey between 2014-2019. The Turkish Lira has dramatically dropped against the US dollar to a record low level in August 2018. The Turkish president accused the American president of economic war, insisting that his country would survive an economic assault, but neglecting to comfort financial markets (worried about the breakdown of the national currency of a crucial emerging economy). Meanwhile, investors feared that a financial crisis could spread throughout Europe. Fears of contagion remained high, although the central bank of Turkey pledged to provide liquidity for Turkish banks. In this background, this paper tries to find if different specifications of univariate GARCH models can anticipate volatility in the foreign exchange market. Our study uses estimates from a symmetric GARCH model, namely GARCH (1,1) for the TRY/USD exchange rate. The data set was obtained from “Investing.com” and covers
 the period January 2014 – to October 2019. This study shows that GARCH (1, 1) can be successfully applied in modeling and forecasting the volatility trend of the currencies and so in exchange rate administration.

Stock market volatility on shipping stock prices: GARCH models approach

One of the most vital commodities is oil and its impact on the global economy is substantial through numerous studies. This paper aims at examining the stock returns of three main global shipping companies in three countries that are based in Germany (Hapag-Lloyd), South Korea (Hyundai) and Taiwan (Yang Ming). The goal of this study is to look into the oil price and shipping stock prices using a range of univariate GARCH models by using daily data from 2017 to 2020. Methodologically, this study employs symmetric and asymmetric GARCH models. Based on the outcome, it shows that the volatility shocks are quite persistent, with coefficients’ volatility exhibits clustering to set up future positions in anticipation of the advance in the stock market. The findings of EGARCH model show the leverage effect and the shock impact are asymmetric with positive shocks have a stronger influence on the volatility of the following period than the same negative magnitudes of shocks. Nonetheless, the GJR-GARCH model presents a rather mixed outcome with Yang Ming exhibits no asymmetric effect on the volatility, while Hyundai and HLAG show that negative shocks led towards volatility. In sum, the finding of this study is important since it would provide a realistic view for regulators and investors in anticipating the market sentiment in shipping market with respect to the global oil prices.

Modeling the Exchange Rate Volatility Using the BRICS GARCH-type Models

The paper models the performance of GARCH-type models on BRICS exchange rates volatility. The levels of interdependence and dynamic connection among the BRICS financial markets using appropriate univariate time series models were evaluated for the period January 2008 to January 2018. The results revealed the presence of ARCH effects in the BRICS exchange rates. The univariate GARCH models for the BRICS exchange rates were fitted to the data using Student t-distribution. The GARCH (1,1) model found the unconditional volatility for each of the BRICS exchange rates series while EGARCH (1,1) and TGARCH (1,1) models presented the leverage effect. Moreover, the EGARCH (1,1) model illustrated that the asymmetric effects dominate the symmetric effects except for South Africa. The TGARCH (1,1) model on the other hand revealed contrary findings. The paper recommends a study be considered to draw comparison on the different types of GARCH models on the time varying integrated data other than the ones used in the paper.

Markov Switching Garch Models: Higher Order Moments, Kurtosis Measures, and Volatility Evaluation in Recessions and Pandemic

In this article, we derive neat matrix formulas in closed form for computing higher order moments and kurtosis of univariate Markov switching GARCH models. Then we provide asymptotic theory for sample estimators of higher order moments and kurtosis which can be used for testing normality. We also check our theory statements numerically via Monte Carlo simulations. Finally, we take advantage of our theoretical results to recognize different periods of high volatility stressing the stock markets, such as financial crisis and pandemic.